Báo cáo hóa học: " Changes in the criticality of Hopf bifurcations due to certain model reduction techniques in systems with multiple timescales" pdf

Journal of Mathematical Neuroscience (2011) 1:9 DOI 10.1186/2190-8567-1-9 RESEARCH Open Access Changes in the criticality of Hopf bifurcations due to certain model reduction techniques in systems with multiple timescales Wenjun Zhang · Vivien Kirk · James Sneyd · Martin Wechselberger Received: 31 May 2011 / Accepted: 23 September 2011 / Published online: 23 September 2011 © 2011 Zhang et al.; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons Attribution License Abstract A major obstacle in the analysis of many physiological models is the is- sue of model simplification. Various methods have been used for simplifying such models, with one common technique being to eliminate certain ‘fast’ variables using a quasi-steady-state assumption. In this article, we show when such a physiological model reduction technique in a slow-fast system is mathematically justified. We pro- vide counterexamples showing that this technique can give erroneous results near the onset of oscillatory behaviour which is, practically, the region of most importance in a model. In addition, we show that the singular limit of the first Lyapunov coefficient of a Hopf bifurcation in a slow-fast system is, in general, not equal to the first Lyapunov coefficient of the Hopf bifurcation in the corresponding layer problem, a seemingly counterintuitive result. Consequently, one cannot deduce, in general, the criticality of a Hopf bifurcation in a slow-fast system from the lower-dimensional layer problem. Keywords Physiological model reduction · geometric singular perturbation theory · Hopf bifurcation · first Lyapunov coefficient · quasi-steady-state reduction W Zhang (  ) · VKirk· J Sneyd Department of Mathematics, University of Auckland, Auckland 1142, New Zealand e-mail: w.zhang@math.auckland.ac.nz VKirk e-mail: v.kirk@auckland.ac.nz J Sneyd e-mail: j.sneyd@auckland.ac.nz M Wechselberger School of Mathematics and Statistics, University of Sydney, Camperdown, NSW 2006, Australia e-mail: wm@maths.usyd.edu.au Page 2 of 22 Zhang et al. 1 Introduction Many models of physiological processes have the feature that one or more state vari- ables evolve much faster than the other variables. Classic examples are neural activ- ities such as bursting and spiking, and intracellular calcium signalling [1]. In many of these models, the time scale separation becomes apparent in the form of a small dimensionless parameter (often denoted by ε) after non-dimensionalisation of the model that brings it into a standard slow-fast form: 1 x  = f(x,z;μ, ε), z  = εg(x, z;μ, ε), (1) where x ∈ R k denotes the fast (dimensionless) state variables, z ∈ R l denotes the slow (dimensionless) state variables, μ ∈R m denotes (dimensionless) parameters of the model, prime denotes differentiation with respect to the fast (dimensionless) time scale t, and ε  1. Such a model has an equivalent representation on the slow time scale τ = εt, obtained by rescaling time and given by ε ˙x = f(x,z;μ, ε), ˙z = g(x,z;μ, ε), (2) where the overdot denotes differentiation with respect to the slow time scale τ . Mod- els with this feature are called singularly perturbed systems and one can exploit the separation of time scales in the analysis of these (k +l)-dimensional models by split- ting the system into the k-dimensional fast subsystem obtained in the singular limit ε →0of(1) and known as the layer problem, and the one-dimensional slow subsys- tem obtained in the singular limit ε →0of(2) and known as the reduced problem. The aim is to make predictions about the dynamics in the full model based on what is seen in the lower-dimensional fast and slow subsystems. Geometric singular pertur- bation theory (GSPT) [2–9] forms the mathematical foundation behind this approach and it is a well-established tool in the analysis of many multiple time scales problems in the biosciences (see, e.g., [1, 10–12]). Perhaps the best-known instance of the use of GSPT in this way is the analysis of the famous Hodgkin-Huxley (HH) model of the (space-clamped) squid giant axon [13] by FitzHugh [14, 15]. The HH model is a four-dimensional conductance-based model in which two state variables (the inactivation gate of the sodium channel h and the activation gate of the potassium channel n) have slow kinetics compared to the other two fast state variables (the membrane potential V and the activation gate of the sodium channel m). Thus, it is possible to split the analysis of this four-dimensional problem into a two-dimensional layer problem and a two-dimensional reduced prob- lem which are amenable to phase-plane analysis. Concatenation of solutions of these two subsystems then allows an explanation of the genesis of, e.g., action potentials observed in the full model. 1 Identifying such a single separation and grouping the state variables roughly into slow and fast families often is a difficult part of the model analysis. Journal of Mathematical Neuroscience (2011) 1:9 Page 3 of 22 FitzHugh [15] and Nagumo [16] introduced a Van der Pol-type two-dimensional model reduction (the now famous FHN model) which captures the essential quali- tative dynamics of the HH model. Rinzel [17, 18] then performed a physiological model reduction of the HH equations to a model with one slow (n) and one fast (V ) state variable that also retained the qualitative behaviour of the neural dynamics ob- served. Rinzel’s first reduction step is to relax the fast gate m instantaneously to its quasi-steady-state value m = m ∞ (V ). This model reduction ‘technique’ of relaxing fast gates to their quasi-steady-states is used in many conductance-based models. In this article, we will show when such a reduction step is mathematically justified and point out some potential problems of this technique. The second reduction step used by Rinzel is based on a numerical observation about the dynamics of the slow variables, namely that there seems to be a (linear) functional relation along the attractor between n and h such that one can replace n by a function of h (FitzHugh [15] observed this as well). This step has no mathematical justification but the two-dimensional model obtained in this way still describes the basic HH model dynamics well. Of course, some transient features of the original model are lost [19] as well as possible chaotic behaviour [20, 21]. These transient features might become important when one models coupled cells where such intrinsic transient dynamics might play a role in forming new attractors. In many physiological models, we are interested in the onset of oscillations, i.e. in the existence and criticality of Hopf bifurcations. The existence and location of any Hopf bifurcations in a model can easily be established by computing the eigenvalues of the system linearised about the equilibrium solutions; a Hopf bifurcation occurs generically when a pair of eigenvalues crosses the imaginary axis under parameter variation. However, determination of the criticality of a Hopf bifurcation typically is more complicated. For a general system, criticality of a Hopf bifurcation is computed using centre manifold theory to reduce the problem to a two-dimensional system, valid near the Hopf bifurcation, and then doing calculations on the model restricted to this two-dimensional centre manifold. These calculations determine the so-called first Lyapunov coefficient for the Hopf bifurcation [22, 23], the sign of which deter- mines whether or not the Hopf bifurcation is supercritical, i.e. which side of the Hopf bifurcation the oscillations appear and whether they are stable on the centre mani- fold. It is desirable that model reductions be performed in such a way that a Hopf bifurcation in the full model corresponds to a Hopf bifurcation in the reduced model and that the criticalities of the bifurcations in the full and reduced models match. We will point out where model reductions may have pitfalls in this respect. For a physiological model given as a singularly perturbed system (1), there is an added complication related to a Hopf bifurcation. Suppose the full system possesses a Hopf bifurcation that persists in the singular limit as a Hopf bifurcation of the layer problem (for k ≥2). We may want to know if one can relate the criticality of the Hopf bifurcation obtained in the layer problem to the criticality of the Hopf bifurcation in the full problem. Care needs to be taken because, very near the Hopf bifurcation, the time scale associated with the bifurcating directions (i.e. corresponding to the real part of the complex conjugate pair of eigenvalues) will be comparable with the time scale(s) associated with the slow variable(s), which can give rise to problems if we wish to apply GSPT. Page 4 of 22 Zhang et al. In this article, we focus on the criticality of Hopf bifurcations in typical physi- ological models with multiple time scales. We show that in some cases in which a Hopf bifurcation involves the fast variables, all the information needed to determine the criticality of the bifurcation is contained in the fast subsystem but in other cases there is crucial information in the slow dynamics that can change the criticality of the Hopf bifurcation, a seemingly counterintuitive result. The outline of this article is as follows. In Section 2, we look at a model reduction technique widely used in the analysis of physiological models that can be written as slow-fast systems, and determine conditions under which the use of this tech- nique can be rigorously justified by centre manifold theory. In Section 3, we focus on Hopf bifurcations in slow-fast systems. After reviewing the general procedure for computing the criticality of a Hopf bifurcation (Section 3.1), we show that the physi- ological model reduction technique considered in Section 2 can change the criticality of a Hopf bifurcation, so that the criticality of a Hopf bifurcation in a model may not match the criticality of the corresponding Hopf bifurcation in the reduced model (Section 3.2). We go on to show that there are potential traps in determining the crit- icality of a Hopf bifurcation when we try to apply GSPT; the criticality of a Hopf bifurcation in the layer problem may not match that of the corresponding Hopf bi- furcation in the full system (Section 3.3), although matters are more straightforward when there is no Hopf bifurcation in the layer problem (Sections 3.4 and 3.5). We il- lustrate our results with numerical examples throughout Section 3. Section 4 contains some conclusions. 2 A physiological model reduction technique for slow-fast systems In this section, we outline a model reduction technique widely used in physiological models that are modelled as slow-fast systems, and find conditions under which the use of this technique is justified. Many physiological models, including many neural and calcium models, contain gating variables m = (m 1 , ,m j ) which are thought to evolve on a time scale which is fast compared with other processes. In these cases, a classic first step is to set the fast gating variables to their quasi-steady-state values, and thereby reduce the dimension of the model by the number of gating variables treated in this way. In this section, we show that this procedure can sometimes be justified by centre and invariant manifold theory. Specifically, we are concerned with physiological models that are described in dimensionless form by singularly perturbed systems of the form v  = f(v,m,n,μ,ε), m  = h(v, m, n, μ, ε), n  = εg(v,m,n,μ,ε), (3) where (v, m) ∈ R × R j = R k are the fast variables, n ∈ R l are the slow variables, f , g and h are order-one vector-valued functions, μ ∈ R m are system parameters, prime denotes differentiation with respect to the fast time t and ε  1 is the singular perturbation parameter reflecting the time scale separation. In neural models, v will Journal of Mathematical Neuroscience (2011) 1:9 Page 5 of 22 typically represent voltage, while in calcium models, v might represent the cytosolic calcium concentration. In biophysical (conductance-based) models, m represents the fast gating variables and n represents the slow gating variables. In calcium models, the total calcium concentration might also be included in the slow variables n. By taking the singular limit ε → 0in(3), we obtain the layer problem, which possesses, in general, an l-dimensional manifold of equilibria called the critical man- ifold, 2 S 0 :={(v,m,n): f(v,m,n,μ,0) =h(v, m, n, μ, 0) = 0}. We are interested in different cases, depending on whether or not the critical manifold is normally hyperbolic, and, if it is not normally hyperbolic, the way in which it fails to be normally hyperbolic. Assumption 1 The critical manifold S 0 is normally hyperbolic, i.e. all eigenvalues of the (k ×k) Jacobian matrix of the layer problem evaluated along S 0 , J =  ∂ ∂v fD m f ∂ ∂v hD m h      S 0 , have real parts not equal to zero. Fenichel theory [2, 3 ] applies under this assumption and we have the following result: Proposition 1 Given system (3) under Assumption 1, then there exists an l- dimensional invariant manifold S ε given as a graph (v, m) = ( ˆ V(n,μ,ε), ˆ M(n, μ, ε)). This invariant manifold is a smooth O(ε) perturbation of S 0 . System (3) reduced to S ε has the form ˙n = g( ˆ V(n,μ,ε), ˆ M(n,μ,ε),n,μ,ε), (4) where the overdot denotes differentiation with respect to the slow time scale τ = εt. Since S ε is a regular perturbation of S 0 , the slow flow (4) on S ε is a regular O(ε) perturbation of the reduced flow on S 0 given by ˙n = g( ˆ V(n,μ,0), ˆ M(n,μ,0), n, μ, 0). (5) If we assume that S 0 is normally hyperbolic with all eigenvalues having real part less than 0, then Proposition 1 implies that a model reduction onto the slow manifold S ε will cover the dynamics of the model after some initial transient time. In a bio- physical model that would imply that the reduction of the fast gating variables m and, e.g., voltage or cytosolic calcium concentration v to their quasi-steady-state values is correct to leading order of the perturbation, i.e. it correctly describes the flow on S 0 . 2 Note that this manifold also represents the phase space for the slow variables n in the other singular limit problem on the slow time scale τ =εt, the reduced problem. Page 6 of 22 Zhang et al. Unfortunately, most physiological models have a critical manifold that is not nor- mally hyperbolic and the reduction technique that Proposition 1 suggests is not (glob- ally) justified. In the following, we focus on the two main cases that cause loss of normal hyperbolicity of S 0 : a fold or a Hopf bifurcation in the layer problem. Assumption 2 The Jacobian of the layer problem evaluated along S 0 , i.e. the (k×k)- matrix J =  ∂ ∂v fD m f ∂ ∂v hD m h      S 0 , has a zero eigenvalue along F := {(v,m,n)∈ S 0 : det(J ) = 0, rank(J ) = j=k −1} which is an (l − 1)-dimensional subset of S 0 . We further assume that the other j eigenvalues all have real parts less than 0 along S 0 . Generically, the manifold S 0 is folded near F if the following non-degeneracy conditions are fulfilled (evaluated along F ): w l ·[(D 2 (v,m)(v,m) (f, h))(w r ,w r )]=0,w l ·[D n (f, h)]=0(6) where w l and w r denote the left and right null vectors of the Jacobian J . Without loss of generality, we assume that the (j ×j) sub-matrix D m h of the Jacobian J has full rank j . This implies that the right nullvector w r of J has a non-zero v-component, i.e. the nullspace is not in v =0. Next we make use of the fact that the determinant of the Jacobian J can be calcu- lated by det(J ) = det(D m h) ·det  ∂ ∂v f −D m f(D m h) −1 ∂ ∂v h  which follows from the block structure of J and the Leibniz formula for determi- nants. By Assumption 2, det(J ) = 0 along F . Since D m h has full rank, det(D m h) = 0 along F . Hence, the second determinant det  ∂ ∂v f −D m f(D m h) −1 ∂ ∂v h  =0 along F which implies that ∂ ∂v f − D m f(D m h) −1 ∂ ∂v h = 0 along F (because it is a scalar). This reflects the zero eigenvalue of J . Since det(D m h) = 0, it also fol- lows from the implicit function theorem that h(v, m, n, μ, ε) = 0 can be solved for m = M(v,n,μ,ε). Note that in neural models this functional relation is auto- matically given by the quasi-steady-state functions m i = M i (v,n,μ,ε) = m i,∞ (v), i = 1 , ,j, for the fast gating variables. In the following, we generalise a result that was presented in [19]fortheHH model (compare also with general results on systems with folded critical manifolds in [9]). Proposition 2 Given system (3) under Assumption 2, then there exists an (l + 1)- dimensional centre manifold W c in a neighbourhood of the fold F given as a graph Journal of Mathematical Neuroscience (2011) 1:9 Page 7 of 22 m = ˆ M(v,n,μ,ε). System (3) reduced to W c has the form v  = f(v, ˆ M(v,n,μ,ε),n,μ,ε), n  = εg(v, ˆ M(v,n,μ,ε),n,μ,ε). (7) Since the right nullvector w r has a non-zero v-component it follows that the one- dimensional centre manifold of the layer problem of (3) is (locally) given as a graph over the v-space. Thus, the corresponding (l +1)-dimensional centre manifold of the full system (3) is also (locally) given as a graph m = ˆ M(v,n,μ,ε). Introducing the nonlinear coordinate transformation ˆm = m − ˆ M(v,n,μ,ε) to system (3)gives v  = f (v,m(v, ˆm,n,μ,ε),n,μ,ε), ˆm  = h(v, m(v, ˆm,n,μ,ε),n,μ,ε) − ∂ ∂v ˆ M(v,n,μ,ε)f (v,m(v, ˆm,n,μ,ε),n,μ,ε), −εD n ˆ M(v,n,μ,ε)g(v,m(v, ˆm,n,μ,ε),n,μ,ε), n  = εg(v, m(v, ˆm,n,μ,ε),n,μ,ε), (8) where the (l +1)-dimensional centre manifold is now aligned with ˆm =0. Hence, the flow on the (l +1)-dimensional centre manifold is given by system (7). This proves the assertion. Note that, in general, M = ˆ M, i.e. solving the equation h(v, m, n, μ, ε) = 0for m = M(v,n,μ,ε) does not yield the centre manifold for any ε, including ε = 0. Thus, the dynamics of the reduced system obtained using the quasi-steady-state re- duction is, in general, different to the dynamics of the full system reduced to the cen- tre manifold. The difference between M and ˆ M is due to two terms: an ε-dependent term that tends to zero in the singular limit and a term that is due to f . This last term will vanish on the critical manifold (where f = 0) and so on the critical manifold, M → ˆ M as ε → 0. In summary, we have shown that making a quasi-steady-state approximation can be mathematically justified if the critical manifold is normally hyperbolic (Proposi- tion 1) or if it loses normal hyperbolicity in a simple fold and we are concerned with dynamics near the fold only (Proposition 2). In these cases, quantitative changes may be introduced by the approximation but the qualitative features of the dynamics will be preserved. 2.1 The Hodgkin-Huxley model As an example of such a model reduction, we look again at the HH model which models the space-clamped squid giant axon. This model is a four-dimensional system Page 8 of 22 Zhang et al. that in dimensionless form is given by εv  = ¯ I −m 3 h(v − ¯ E Na ) −¯g k n 4 (v − ¯ E k ) −¯g l (v − ¯ E L ) ≡ S(v, m, n, h), εm  = 1 τ m t m (v) (m ∞ (v) −m) ≡ M(v,m), h  = 1 τ h t h (v) (h ∞ (v) −h) ≡ H(v,h), n  = 1 τ n t n (v) (n ∞ (v) −n) ≡ N(v,n), (9) where the fast variables are v and m (dimensionless membrane potential and activa- tion gate of the sodium channel) and the slow variables are h and n (inactivation gate of the sodium channel and activation gate of the potassium channel). The quantity ¯ I is the bifurcation parameter (and is proportional to the applied external current I ), and expressions for the functions m ∞ (v), n ∞ (v), h ∞ (v), etc. and the values of constants used in (9) are given in the Appendix. It was shown in [19] that the two-dimensional critical manifold is cubic-shaped in the physiologically relevant domain of the phase space, with two fold-curves F ± , attracting outer branches and a middle branch of saddle type. Furthermore, the vec- tor field has a three-dimensional centre manifold m = ˆ M(v,n,h,ε) along each fold curve F ± , which is exponentially attracting. Hence, Proposition 2 can be applied and the vector field reduced to the centre manifold near each fold F ± is given by εv  = ¯ I − ˆ M 3 (v,n,h,ε)h(v− ¯ E Na ) −¯g k n 4 (v − ¯ E k ) −¯g l (v − ¯ E L ), h  = 1 τ h t h (v) (h ∞ (v) −h), n  = 1 τ n t n (v) (n ∞ (v) −n). (10) One of the classical reduction steps in the literature is to use the quasi-steady-state approximation m = m ∞ (v) rather than perform the full centre manifold reduction m = ˆ M shown above. We have to expect quantitative changes in the reduced model (i.e. in Equations (10) with ˆ M(v,n,h,ε) replaced by m ∞ (v)) compared to the full HH model (9), and such changes are in fact observed. For example, (9) has a sub- critical Hopf bifurcation for I = 9.8 μA/cm 2 (i.e. ¯ I = 0.00082) while (10) with ˆ M = m ∞ has a subcritical Hopf bifurcation for I = 7.8 μA/cm 2 (i.e. ¯ I = 0.00065). We note that the Hopf bifurcation of (9) is in the vicinity of the fold curve for suffi- ciently small ε, because in the singular limit the bifurcation is a singular Hopf bifur- cation [19, 24]. Thus, the Hopf bifurcation in (9) is in the regime covered by Proposi- tion 2. Further discussion of this type of Hopf bifurcation is contained in Section 3.4. 3 Hopf bifurcation in slow-fast systems In the previous section, it was shown that the quasi-steady-state reduction technique is mathematically justified in a slow-fast system if the critical manifold is normally hy- Journal of Mathematical Neuroscience (2011) 1:9 Page 9 of 22 perbolic or if we are interested in the dynamics near a simple fold of the critical man- ifold. In this section, we show that the model reduction technique discussed above, when applied to slow-fast systems with a Hopf bifurcation, may lead to changes in the criticality of the Hopf bifurcation. From a dynamical systems point of view, it is well established that misleading results can be obtained if a proper centre manifold reduc- tion is not performed prior to the identification of bifurcations [22, 23]. However, in the context of biophysical systems, model variables often have a direct physiological meaning and so it is tempting to try to avoid making coordinate transformations that combine the variables into physically ambiguous combinations. (Transformations re- quired for centre manifold reductions are frequently of this type.) Unfortunately, this has resulted in some erroneous conclusions in the literature about the criticality of Hopf bifurcations in some biophysical models, as we will show in this section. We then go on to show that there can be problems with the use of GSPT in analysing models with Hopf bifurcations, and in particular show that the critical- ity of a Hopf bifurcation in a full slow-fast system may not match the criticality of the corresponding Hopf bifurcation in the associated layer problem. This last result is independent of whether a quasi-steady-state assumption or other reduction technique has been used prior to applying GSPT. 3.1 Computing the criticality of a Hopf bifurcation We first give a brief review of the general procedure for computing the criticality of a Hopf bifurcation. The criticality of a Hopf bifurcation is determined by the sign of the first Lyapunov coefficient of a system near a Hopf bifurcation [23, 25, 26]. Specifically, consider a general system x  =f(x;μ), with x ∈ R n , μ ∈ R and with a Hopf bifurcation at x = 0, μ =ˆμ. Write the Taylor expansion of f(x;ˆμ) at x = 0as f(x;ˆμ) =Ax + 1 2 B(x,x) + 1 6 C(x,x,x) +O(x 4 ), where A is the Jacobian matrix evaluated at the bifurcation, and B(x,y) and C(x,y,z) are multilinear functions with components B j (x, y) = n  k,l=1 ∂ 2 f j (ξ;ˆμ) ∂ξ k ∂ξ l      ξ=0 x k y l , (11) C j (x,y,z)= n  k,l,m=1 ∂ 3 f j (ξ;ˆμ) ∂ξ k ∂ξ l ∂ξ m      ξ=0 x k y l z m , (12) where j = 1, 2, ,n.Letq ∈ C n be a complex eigenvector of A corresponding to the eigenvalue iω,i.e.Aq = iωq.Letp be the associated adjoint eigenvector, i.e. Page 10 of 22 Zhang et al. p ∈ C n and A T p =−iωp, p, q=1. Here p, q= ¯p T q is the usual inner product in C n . Then the first Lyapunov coefficient for the system is defined as l 1 = 1 2ω Re  p,C(q,q, ¯q)−2p,B(q,A −1 B(q, ¯q)) +p,B( ¯q,(2iωI n −A) −1 B(q,q))  , (13) where I n is the n×n identity matrix. If l 1 < 0 the Hopf bifurcation is supercritical and produces periodic solutions that are stable on the two-dimensional centre manifold corresponding to the Hopf bifurcation. If l 1 > 0, the Hopf bifurcation is subcritical and the associated periodic orbits are unstable within the centre manifold. 3.2 Hopf bifurcations and model reduction Here we are concerned with physiological models that are of the same form as (3) except that v is now in R 2 instead of in R. Specifically, we are interested in models that are described in dimensionless form by singularly perturbed systems of the form v  = f(v,m,n,μ,ε), m  = h(v, m, n, μ, ε), n  = εg(v,m,n,μ,ε), (14) where (v, m) ∈ R 2 × R j = R k are the fast variables, n ∈ R l are the slow variables, f , g and h are order-one vector-valued functions, μ ∈ R m are system parameters and ε  1 is the singular perturbation parameter reflecting the time scale separation. Without loss of generality, we fix m − 1 parameters, and consider Hopf bifurcations that occur as the other parameter, which we denote by ν, is varied. Assumption 3 System (14) possesses a non-degenerate Hopf bifurcation at ν =ˆν ε . Specifically, for sufficiently small ε: (a) there exists a family of equilibria (v(ν, ε), m(ν, ε), n(ν, ε)), for ν in a neigh- bourhood of ˆν ε , such that the Jacobian matrix has a pair of eigenvalues, λ 1 (ν) and λ 2 (ν), with λ 1 (ˆν ε ) = ¯ λ 2 (ˆν ε ) = iω where ω =O(1), while the other (k − 2) eigenvalues associated with the fast components of the vector field all have real parts of order O(1), which we assume to be negative; (b) d dν Re(λ 1 )| ν=ˆν ε =O(1) = 0; (c) l 1 (ε) = O(1) = 0, where l 1 is the first Lyapunov coefficient associated with the Hopf bifurcation; (d) the bifurcation parameter ν persists in the singular limit ε → 0, i.e. ν appears explicitly in the layer problem. The condition ω =O(1) ensures that the Hopf bifurcation is in the fast variables. Thus, there is a Hopf bifurcation for ν =ˆν 0 in the singular limit system of (14), the layer problem. 3 We assume, without loss of generality, that the complex eigenvector 3 In fact, there will be a manifold of Hopf bifurcations in the layer problem, one associated with each choice of the (fixed) slow variables. We are concerned only with the Hopf bifurcation of the distinguished [...]... bifurcations in the layer system and the full system, and there is no reason to expect the criticality of the Hopf bifurcations to be the same for the full system and the layer problem Knowledge of the dynamics in the layer problem is therefore insufficient to predict the criticality of the Hopf bifurcation in the full system The Chay-Keizer model discussed above provides another example of a specific model with. ..Journal of Mathematical Neuroscience (2011) 1:9 Page 11 of 22 q ∈ Ck of the eigenvalue iω in the layer problem of (14) has non-zero entries in the first two fast components of the vector field, v ∈ R2 , i.e we associate the Hopf bifurcation with the direction of v A natural first step in determining the criticality of the Hopf bifurcation in the full system (14) might be to reduce the dimension of the model. .. and there is a Hopf bifurcation in the reduced problem (i.e λ = ±iω with ω = O(ε)), the criticality of the Hopf bifurcation will be the same in the full system and the reduced problem In recent study [38], Guckenheimer and Osinga investigate two slow-fast systems in which the criticality of a Hopf bifurcation in the full system does not match the criticality of the corresponding Hopf bifurcation in the. .. criticality of the associated Hopf bifurcation in the layer problem However, in the special case that the component of the vector field associated with the slow variable is sufficiently aligned with the centre manifold of the full system (17) then there is no such difficulty; the criticality of the Hopf bifurcations in the ε = 0 limit of the full system and in the layer problem will match Page 16 of 22 Zhang... match the dynamics of the full model in the limit that we approach the layer problem We have shown that this is not the case, at least for the criticality of Hopf bifurcations There are no such difficulties in computing the criticality of Hopf bifurcations that involve slow variables We discussed two cases The first case occurs when the Hopf bifurcation in the full model is caused by the interaction of. .. about the criticality of the Hopf bifurcation in the full system In some biophysical models, the layer problem corresponds to a physically distinct state of the system For example, in models of intracellular calcium dynamics, the layer problem frequently can be thought of a modelling the cell with no flux across the cell membrane In such a situation, it is tempting to presume that the dynamics of the. .. can alter the criticality of Hopf bifurcations in the system, so that a subcritical Hopf bifurcation in the full system becomes a supercritical Hopf bifurcation in the reduced system, or vice versa If the purpose of analysis is to determine the nature of the onset of oscillations, it may not be advisable to perform a quasi-steady-state reduction We note that a change in the criticality of the Hopf bifurcation... με of the full system is, in general, different ˆ to the bifurcation value μ = μ0 of the layer problem More importantly, we show ˆ Page 14 of 22 Zhang et al that the O(ε) terms in the slow equation can produce an O(1) change in the first Lyapunov coefficient which in turn may lead to a change of the criticality of the Hopf bifurcation in the full system compared with the criticality of the associated Hopf. .. bifurcation in the layer problem It might be tempting to proceed by determining the nature of the Hopf bifurcation in the layer problem and then asserting that the Hopf bifurcation in the slow-fast system will be of the same type However, the existence of a Hopf bifurcation satisfying Assumption 3 automatically implies that the critical manifold of the full system is not normally hyperbolic near the bifurcation,... (O(ε)) terms of the slow c equation in (15) play an important role in the calculation of the first Lyapunov coefficient In the following, we show how these small O(ε) terms can be significant in determining the criticality of a Hopf bifurcation in a slow-fast system In particular, we show that calculating the first Lyapunov coefficient l1 (ε) of a Hopf bifurcation in the ˆ full system and then taking the limit . Journal of Mathematical Neuroscience (2011) 1:9 DOI 10.1186/2190-8567-1-9 RESEARCH Open Access Changes in the criticality of Hopf bifurcations due to certain model reduction techniques in systems with multiple. Hopf bifurcation in the layer problem. It might be tempting to proceed by determining the nature of the Hopf bifurcation in the layer problem and then asserting that the Hopf bifurcation in the. components of the vector field, v ∈ R 2 , i.e. we associate the Hopf bifurcation with the direction of v. A natural first step in determining the criticality of the Hopf bifurcation in the full system